IAB Discussion Paper 13/2012doku.iab.de/discussionpapers/2012/dp1312.pdfIAB Discussion Paper Articles on labour market issues 13/2012 Alexandra Kröll Oliver Farhauer Examining the

IAB Discussion PaperArticles on labour market issues

13/2012

Alexandra Kröll Oliver Farhauer

Examining the roots of homelessnessThe impact of regional housing market conditions and the social environment on homelessness in North Rhine-Westphalia, Germany

IAB-Discussion Paper 13/2012 2

Examining the Roots of Homelessness The Impact of Regional Housing Market Conditions and the Social Environment on Homelessness in North Rhine-Westphalia, Germany

Alexandra Kröll (Lehreinheit für VWL, Universität Passau) Oliver Farhauer (Lehreinheit für VWL, Universität Passau)

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Contents

Abstract ...................................................................................................................... 4

Zusammenfassung ..................................................................................................... 4

1 Introduction ............................................................................................................ 5

2 The Model .............................................................................................................. 8

3 Data and Methodology......................................................................................... 13

4 Results ................................................................................................................. 15

5 Conclusion ........................................................................................................... 19

References ............................................................................................................... 20


Abstract Despite large-scale governmental efforts to combat homelessness, homelessness rates can only be reduced but not eliminated completely by the measures usually applied. Hence, there is an obvious need to investigate additional factors which con-tribute to homelessness and gain insights on how to further reduce homelessness. To begin with, the relationship between the conditions prevailing on the housing market and homelessness levels is made out with the help of a theoretical model. From this model, a critical income ensuring positive housing consumption can be deduced; individuals with an income below this critical threshold end up homeless. The empirical analysis draws on a panel data set comprising information on all dis-tricts (Kreise) of North Rhine-Westphalia from 2004-2009. The regression analysis underpins the theoretical results: High (net market) rents as well as low vacancy rates among small flats lead to rising homelessness. Homelessness also increases when the share of long-term unemployed and of those with a monthly income below € 700 is higher, since this makes it more difficult to reach the critical income needed to rent a flat. Finally, some policy conclusions resulting from the analysis are pointed out.

Zusammenfassung Trotz umfangreicher staatlicher Programme zur Bekämpfung von Obdachlosigkeit kann diese mit den üblicherweise eingesetzten Mitteln nur verringert, aber nicht gänzlich beseitigt werden. Deshalb ist es wichtig, zusätzliche Faktoren, die zur Ent-stehung von Obdachlosigkeit beitragen, zu untersuchen und so Ansatzpunkte zu deren weiterer Reduktion auszumachen. Zunächst wird modelltheoretisch der Zu-sammenhang zwischen den auf dem Wohnungsmarkt vorherrschenden Rahmenbe-dingungen und der Obdachlosigkeit hergestellt. Aus dem Modell lässt sich ein kriti-sches Einkommen, das den Konsum von Wohnraum gewährleistet, ableiten; Indivi-duen mit einem Einkommen unterhalb dieser kritischen Schwelle sind obdachlos. Die empirische Überprüfung des Modells erfolgt anhand eines Paneldatensatzes mit Informationen für die Kreise Nordrhein-Westfalens für die Jahre 2004 bis 2009. Die Regressionsanalysen bestätigen die aus dem Modell abgeleiteten Erkenntnisse: Teurer Wohnraum (gemessen an den Nettoangebotsmieten) sowie geringer Leer-stand an kleinen Wohnungen führen zu erhöhter Obdachlosigkeit. Die Obdachlosig-keit steigt ebenfalls, je höher der Anteil der Langzeitarbeitslosen und derjenigen mit einem monatlichen Einkommen unter € 700 ist, da hierdurch das Erreichen der kriti-schen Einkommensschwelle schwieriger wird. Abschließend werden einige, sich aus der Analyse ergebende, Politikimplikationen aufgezeigt.

JEL classification: R21, R31, R38, I38

Keywords: Homelessness, housing markets, regional social environment, long-term unemployment

Acknowledgements: The authors would like to express their gratitude to Lutz Ei-genhüller, Stefan Fuchs, Brendan O’Flaherty, Daniel Werner, Roland Žulić, the teams of the Federal Institute for Research on Building, Urban Affairs and Spatial Development, and IT.NRW (Statistics Agency of North Rhine-Westphalia).


1 Introduction Even in industrialised countries with comprehensive social security systems, poverty continues to be a widespread phenomenon. One of the most severe forms of pov-erty expresses itself in homelessness. Because of the gravity of this situation for those affected, homelessness deserves special attention. Previous studies highlight that there are many causes for becoming homeless. We focus on some particularly important reasons – of economic as well as of social nature – for becoming/remain-ing homeless, embed them into a theoretical model and then go on to study their impact on homelessness in Germany. Employing a panel data set, the aim of this paper is to assess the influence of variables capturing the conditions on the housing market and the social environment on the number of homeless people per inhabi-tant. The area under consideration is North Rhine-Westphalia, because it is the only German state (Bundesland) keeping an official record of its homeless. North Rhine-Westphalia is densely populated, and it is the largest German state in terms of population numbers (roughly 18 million inhabitants as compared to about 82 million inhabitants nationwide).

Overall, the number of homeless people in North Rhine-Westphalia strongly fell from 18,533 in 2004 to 11,788 in 2009, as shown by the black line (left axis) in Figure 1. This corresponds to a 36 % decrease relative to initial levels in 2004. For investigat-ing whether this result is characteristic for only a few districts (Kreise) – or rather shows a general trend –, the development at the spatially more disaggregated dis-trict level needs to be considered: In 25 out of 53 districts of North Rhine-West-phalia, the number of homeless people fell continuously from 2004 to 2009. In an-other 25 districts, the number of homeless rose only once or twice in this interval (and fell in the remaining years), whereas it increased a maximum of three times in only three districts. These results still hold when accounting for changes in popula-tion size. The grey line (right axis) in Figure 1 represents the number of homeless people per thousand inhabitants for North Rhine-Westphalia as a whole; it is con-tinuously decreasing over the time period considered, from 1.03 in 2004 to 0.66 in 2009. At district level, the pattern of change is almost exactly the same as when looking at absolute numbers.

Being homeless is an extreme situation with negative implications on both physical and psychological health (for Germany see e.g. Fichter/Quadflieg 2001, Salize et al. 2002). The average life expectancy of homeless people in Germany is about 10 years lower than of those who are not, and results for London show that the life ex-pectancy of rough sleepers is only 47 years (Daly 1993). Therefore, an important objective of any government’s social policy should not only be to reduce homeless-ness, but to intervene much sooner and prevent people from becoming homeless in the first place. In 1996, the state government of North Rhine-Westphalia imple-mented a program to avoid homelessness. In short, the program aims at providing consulting services to homeless people and to people who are at risk of becoming homeless, and it promotes housing projects for cases of need. Furthermore, the


program offers assistance specifically targeted at women, since they are often reluc-tant to fall back on mixed-gender facilities (for greater detail, see Enders-Dragässer/ Huber/Sellach 2004, Ministry for Generations, Family, Women and Integration of North Rhine-Westphalia 2007). Much of the marked decrease in homelessness shown in Figure 1 can certainly be attributed to the persistent efforts in the frame-work of this program.1 Nevertheless, the number of homeless still has not come even close to zero. Thus, it is important to further analyse the various factors influ-encing homelessness in order to develop additional programs in both reducing and preventing homelessness. Several important social and economic factors impacting homelessness have been identified. So, there is a pronounced impact of the hous-ing market on homelessness, especially in Germany (Busch-Geertsema/Fitzpatrick 2008: 79). Busch-Geertsema (2005: 9) shows that in times when the housing market tightens and vacancy rates fall, the number of homeless people goes up, and the other way around. Furthermore, there is evidence that only about 6 % of the home-less are employed, although more than 80 % of them would basically be fit for work, and almost 60 % of the homeless receive social benefits (BAGW 2009). This under-pins the need to also include social variables into the analysis.

Figure 1 Homelessness in North Rhine-Westphalia, 2004-2009

0.4

0.6

0.8

1

1.2

11,000

13,000

15,000

17,000

19,000

2004 2005 2006 2007 2008 2009

Homeless population in absolute terms Homelessness rate (per 1,000 inhabitants)

Year

Hom

eles

spo

pula

tion

Hom

eles

snes

s ra

te

(per

1,0

00 in

habi

tant

s)

Source: Authors’ calculations, data from the Statistics Agency of North Rhine-Westphalia

1 One can be pretty sure that the reduction in homelessness is not due to migration, be-

cause homeless people generally are very immobile in a spatial sense, even within their own district. Most homeless people remain in the same locality for many years, as they find easier access to soup kitchens and emergency shelters. Also, spatial mobility of homeless across districts is limited, as they generally cannot afford bus or train tickets. What is more, hardly any homeless person owns a bicycle, which severely restricts mobil-ity (Neupert 2010: 17).


Previous studies have found that besides social factors, economic factors are among the main drivers of homelessness. Wood et al. (1990) conducted a survey in Los Angeles, California, and compared homeless families to poor housed families. It turned out that apart from interpersonal problems and social isolation, high housing costs and family poverty were reported to be the main cause for losing one’s home. Another study employing micro-data is the one by Early (1999) who considers 15 cities in the U.S. He shows that the number of homeless increases the more shel-ters are provided and the better the quality of those shelters is, whereas - in contrast to many other studies (see below) - he finds that an increase in the amount of avail-able housing with minimum quality only plays a minor part in reducing homeless-ness.

Using data not on the individual but on the aggregate level, Honig/Filer (1993) show for 50 metropolitan areas in the U.S. that homelessness is the higher the higher the rents for the cheapest flats are. Vacancy rates of the cheapest flats, on the other hand, do not have a statistically significant influence on the number of homeless. Also considering the U.S., Park (2000) finds that the rates of homelessness rise with tightening conditions on the market for down-market flats. Quigley/Raphael/Smo-lensky et al. (2001) come to a similar conclusion analysing the U.S. as a whole and, in greater detail, California: Low vacancy rates as well as high housing costs both lead to rising homelessness. Mansur et al. (2002) use a general equilibrium simula-tion model to make out the impact of policy interventions concerning the housing market in four California metropolitan areas. Their results, too, highlight the impor-tance of economic drivers of homelessness such as the level of rents in the lower segment of the housing market and the distribution of income. They also show that in the area under scrutiny, the number of homeless can be reduced more effectively by demand-side - instead of supply-side - subsidies. Demand side policies comprise rent subsidies to all poor households to ensure their income meets a certain thresh-old (analytically derived in our model below) so they can afford to buy housing. Sup-ply side subsidies, on the contrary, are general landlord maintenance subsidies which, basically, could also reduce homelessness in that they might reduce rents. However, the former instrument is much more effective in reducing homelessness than the latter, holding the total amount of the subsidy constant.

The present study stands out from the previous contributions for several reasons: Above providing a theoretical model of homelessness, we also test the predictions of this model empirically. In Section 2, the relation between housing market condi-tions and available income as factors driving homelessness is highlighted in a theo-retical model considering the influence of prices for housing, vacancies and a critical income needed to be able to afford housing. Section 3 provides information on the data and an outline of the methodology applied. Using panel data techniques, a data set including observations for all districts of North Rhine-Westphalia from 2004-2009 is analysed. The regression results are presented in Section 4, and the last Section summarizes the most important results from which we can draw several policy con-clusions to tackle homelessness.


2 The Model We - like Honig/Filer (1993) - assume that the homeless and those who are at risk of becoming homeless would consider renting a flat rather than buying one. Thus, the model is concerned with the rental market for flats. In modelling the demand side, we build on the framework set up by O’Flaherty (1995), which we extend in several respects. Consumers have well-behaved preferences which are characterised by a continuous utility function that is defined over the consumption of two goods:

. These two goods which can (but do not necessarily have to) be consumed

– are housing and a composite good which comprises consumption of eve-

rything other than housing and is chosen as the numéraire. The utility function ex-hibits weakly increasing marginal returns in both arguments and is twice continu-ously differentiable, i.e. and as well as . Although all

consumers are said to have the same utility function, they may well differ in incomes and, therefore, in utility levels.

An individual with positive housing consumption , i.e. he or she is not home-

less, consumes an amount of housing – which may either be the number of rooms

or the square metres of a flat – of some quality . Thus, “gross” housing consump-

tion is given by . There is a continuum of qualities of housing, so that there

is no need to be concerned with step size in determining . The median quality of

housing is normalised to one . Hence, housing consumption of an individ-

ual living in a flat of median quality and size is . If instead he/she lived in a

flat of the same size , but of quality below the median, this would result in

and, therefore, show as lower housing consumption. Put differently, if housing of some quality below is consumed, this has the same impact on utility (more pre-

cisely, on which enters the utility) as if less housing was consumed, i.e. if was

smaller. The same holds true for the inverse. This approach is a modelling tool which ensures that there is no need to consider different prices for different housing qualities; the price of one quality-adjusted unit of housing is simply ,

which is taken as given for the moment. If an individual consumes the quantity of

high-quality housing , his or her expenditures on housing will be higher than those

of another individual consuming the same quantity housing of median quality ,

as and, consequently, total expenditures on housing are given by

.

Next, the semi-direct utility of gross housing consumption for a consumer with the income , given the price is defined as

,


using the budget constraint , which is binding in the optimum. The gross

housing demand set for an individual with the income , given prices , is then char-

acterised by

.

The demand set exists and is a singleton because we assumed a utility function that is strictly quasiconcave in (and in as well). An individual is homeless, if zero is

the only element of his/her gross housing demand set. Since , this is the

case if preferences are such that , i.e., either quality zero of housing is

consumed , or no positive flat size is consumed , or both are true

. The homeless bid-rent curve reflects the maximum amount an

individual with the income is ready to pay for the amount of housing. Therefore,

he/she is indifferent between being homeless and paying the sum for

gross housing consumption . Formally, this indifference relation can be stated as

(1) ,

where, again, the budget constraint is used and the bid-rent function is substituted for the price . The bid-rent function is continuous and twice differentiable in both

of its arguments. Furthermore, the function is concave because of the positive, but diminishing, marginal utility of housing and, obviously, for all .

The higher the income , the higher the homeless bid-rent curve for , i.e.

richer people are always willing to pay more on any given positive in order to

avoid being homeless. This is shown in the following: Differentiating the indifference relation in (1) with respect to income yields

(2) .

By making use of (derived from ), simplifying the last term

in (2) and applying different notation, we get

,

and rearranging yields

.

With diminishing marginal utility, the increase in utility caused by a marginal in-crease of -consumption is lower if the whole income is already spent on as com-


pared to a situation in which both goods are consumed in positive amounts, i.e. . Consequently, - which gives the reaction of the bid-rent corre-

sponding to a change in income - is always greater than zero for any . There-

fore, richer people with higher incomes have higher bid-rent functions (for any posi-tive ) and are willing to spend more on a given amount of gross housing consump-

tion in order to avoid being homeless than the less wealthy do.

A critical income can be determined, individuals with incomes below (above) this

threshold value being homeless (tenants). Figure 2 illustrates bid-rent curves

corresponding to different incomes as well as total expenditures on housing at

market price , depending on the amount of gross housing consumed . If an indi-

vidual’s income falls below the threshold value , expenditures shift away from

housing to the composite good, and no housing at all is consumed any more: Indi-viduals with bid-rent curves which are strictly below (at some point above) the line

- like the one corresponding to income -, are homeless (tenants), since

their maximum willingness to pay for housing is lower (higher) than the going rate for all (some) . The critical income corresponds to the lowest bid-rent curve which

just touches the housing expenditure line. An individual with the income is indif-

ferent between spending the amount on housing consumption and being home-

less. Analytically, the critical income is determined by (3) which could be solved for , if a specific function for the bid-rent curve was proposed.

(3) ,

where is the minimum amount of gross housing traded on the market: In order

to be lettable, a flat has to meet some requirements with regard to quality, e.g. the roof must be leak-proof, the windows must be windproof and the flat needs to be connected to the mains. These minimum requirements for quality are denoted by

. In addition, a flat must be of some minimum size , at least a bed and

some essentials must fit in. Thus, denotes the lowest level of gross housing

consumption which could possibly be offered to let. Suppose was smaller than

; then, the smallest supplied flat would be smaller than the smallest demanded

housing unit. In the end, no flats of a size in between and will be supplied

any more, as there will be no demand of such flats at the market price . Moreover it

is impossible that , because supply and the price line only start at

which is why there cannot be any intersection of the price line and any bid-rent curve to the left of and must hold. Intuitively, those whose optimal

gross housing consumption is lower than remain homeless because their spe-


cific demand is not met by the market supply. The previous argument, however, shows that it is impossible (in the long run) that . Consequently,

must be equal to .

Figure 2 Graphical derivation of the critical income

pwExpenditures on w

wwmin = wh

b(w|yh)

b(w|y1)

b(w|y2)

Source: Authors’ illustration

Further below, the aggregate demand for gross housing consumption is derived as the sum of the micro-founded individual demands. The aggregate demand can then be combined with aggregate supply, to determine the equilibrium on the housing market. With regard to the supply-side, we look at the short-run supply of housing, so there is no need to consider construction, decay and maintenance.2 In fact, the model could be combined with any long-run supply-side framework that takes into account different qualities of flats (e.g., the model developed by O’Flaherty, 1995). We abstain from doing so for several reasons: With regard to the supply-side, the most important aspect is the existence of vacancies. This feature can be easily in-corporated into the model when looking at the short run only. Moreover, the prior aim of this paper is not to provide a fully worked-out model of the housing market, but to investigate homelessness in a reasonably realistic setting, which is why our model is kept as simple as possible.

In the short run, the supply of housing is fixed because both, building and deprecia-tion, take time. To incorporate vacancies into the model, imperfect information on the part of consumers is assumed.3 Potential tenants are not perfectly informed about all vacancies, because it is too costly or too complicated to gain knowledge about all vacancies – some are only put up on the bulletin board of a handful of su-

2 Qualitatively, though, the results would be the same if the supply function was assumed

to have a positive slope instead. 3 Also, a matching function could be applied to introduce vacancies into the model.


permarkets, others are only advertised in a limited number of newspapers, while still others are only conveyed through a real estate agent. It is unlikely that the owner of a flat will use all of these channels to advertise the flat and, furthermore, it is also unlikely that a potential tenant will use all of those channels to gain information about vacancies. E.g. somebody moving to a new city will probably not scan all su-permarket bulletin boards in the new area. This is why the actual aggregate supply

will be greater than the aggregate supply perceived by potential tenants, as is

shown in Figure 3. Perceived aggregate supply is a fraction of actual aggre-

gate supply. The parameter depends on the degree of imperfect information; the

more means of advertising home-owners deploy and the more sources of informa-tion potential tenants make use of, the lower is the degree of imperfect information, i.e. the lower is .

The vertical axis plots the price for one quality-adjusted unit of housing (see above), as the horizontal axis plots the aggregate amount of gross housing supplied/ demanded. The total amount of gross housing traded on the market is given by

. Each flat consists of a certain amount of gross housing

(depending on its quality and size); summing over all flats that are at least of the

minimum gross size yields the aggregate amount of gross housing traded on

the market. The aggregate vacant living space equals the difference between actual and perceived aggregate supply and is given by .

Figure 3 The housing market

p

Σiwi for wi ≥wmin

p

aggregate demand

perceived aggregate supply

aggregate supply

aggregate vacant living space

Source: Authors’ illustration

Aggregate demand is a function of the aggregate amount of gross housing de-manded, i.e. the sum of all individuals’ optimal amount of housing consumed:


; it does not need to be linear, as exemplarily depicted in

Figure 3. The only conditions imposed on demand are that it is a strictly monotonic decreasing function and that it is non-zero at the intersection with the perceived ag-gregate supply curve. This intersection determines the equilibrium on the housing market, where the aggregate demand equals the perceived aggregate supply of housing: . The resultant equilibrium price for one quality-

adjusted unit of housing is taken as given by all individuals: By assumption, there are a great many flat-owners and tenants, so none of them can influence the market price through individual action. Despite the price-taking behaviour, the housing mar-ket is not perfectly competitive because of the imperfect information introduced above. Due to imperfect information on the part of the consumers, vacancies can be introduced in the model. Empirically, vacancy rates (among small flats) are an im-portant determinant of homelessness levels (see the empirical results below).

3 Data and Methodology The dependent variable in the regression analyses is the number of homeless per inhabitant. North Rhine-Westphalia is Germany’s only state keeping an official re-cord of the number of homeless people, which is why we use data from this state’s Statistics Agency. Our analysis covers all 53 districts (conforming to the territorial average in 2009) over the years from 2004 to 2009. Every year on June the 30th, the local authorities of North Rhine-Westphalia report to the Statistics Agency the num-ber of homeless living in their area of responsibility. “Homeless” are those who ei-ther have no reasonable accommodation at all or are on the verge of losing it, those who do not have a flat and temporarily live in a shelter, as well as those who cannot – for whatever reason – provide themselves with accommodation at their own ex-pense. The statistics report those homeless people who sleep in government or charity provided shelters and those who are placed in a state-financed flat.

To identify factors influencing the number of homeless people, several independent variables are employed. We look at the level of net market rents, the vacancy rate among small flats and - for depicting a district’s social environment - the share of residents with a monthly income below € 700, the share of long-term unemployed and the share of highly skilled residents are included into the analysis. These vari-ables are described below and interpreted extensively in the results section.

Data on the net market rents of flats are provided by the Federal Institute for Re-search on Building, Urban Affairs and Spatial Development (Bundesinstitut für Bau-, Stadt- und Raumforschung). There, only net basic rents per square metre for non-furnished flats from 40 to 130 m² in size which have not been advertised for more than one year (i.e., they are no slow sellers) are considered. There is not any infor-mation on flats smaller than 40 m², but there is also no reason to believe that net basic rents per square metre may have differed among flats of different size. Con-sequently, it is assumed that the available data are representative for small flats too. The data are collected from advertisements in newspapers and internet platforms


which is why the actual rents after negotiations may be slightly lower than those recorded.

In Germany, everybody who is in need is entitled to public transfers for covering real housing expenditures. The state authorities pay the cost of living of unemployed persons (Kosten der Unterkunft), whereas those who are not unemployed but can-not afford their flat anymore receive housing subsidies (Wohngeld). However, these transfers come along within certain limitations: Only the basic needs of the transfer recipient are covered, and the authorities only pay the costs actually incurred. There are no lump-sum transfers, but instead the transfer level is decided on in every indi-vidual case, so levels differ between recipients and also from district to district, as rents also vary by district. A district’s rent level is a decisive determinant for the level of transfers granted. I.e., if rents are high in a given district, it follows that housing transfers will also be comparatively high in that very district. As a consequence, we do not directly include these transfers to avoid multicollinearity, as the level of net market rents captures the same effect as would transfers.4

A further variable capturing the conditions on the housing market is the empirica vacancy index which provides information on flat vacancy rates. The category of the smallest flats for which this index is available is those for flats with less than 50 m² in size. The number of vacancies from which the vacancy index (vacant flats divided by the whole stock of flats) is computed is rounded to 100. Furthermore, the index is only based on a sample, which is why for some districts there are statistically uncer-tain values due to a low number of cases and for a handful of districts the values are missing at all. Nevertheless, the vacancy index is a useful tool for assessing the approximate scale of vacancy rates.

Besides housing market variables, further variables depicting the social environment which impacts the incidence of homelessness in the different districts are included. One of those variables taken into account is the number of long-term unemployed relative to the total county population. In order to be considered long-term unem-ployed, a person has to experience a (consecutive) unemployment spell for more than one year (data from the Federal Employment Agency). The total district popula-tion is chosen as a reference mark instead of the dependent working population, since the aim is not to depict the conditions on the labour market, but to analyse the social environment of the entire district population. We look at the long-term unem-ployed, because often being long-term unemployed is a precursor to being home-less. Ideally, it would be preferable to also run a regression analysis controlling for the share of unskilled people, because most of the homeless are low-skilled. How-ever, due to the limited character of the data available, this goes along with a major

4 In spite of public housing transfers, people may become homeless if they are heavily

indebted and make improper use of transfers, or they do not even have a bank account where the transfers could go to. Still for others, it might be too complicated to complete all the paperwork needed to register for transfers.


identification issue: The data provided by the Statistics Agency of North Rhine-Westphalia only display the stock of residents without school-leaving or training qualification, but it is impossible to deduce from them who of them is still in school or training. Therefore, we consider instead the share of those with the highest possible qualification obtained in the population of the district as a whole. They either hold a university or specialist college degree or have completed advanced training to be-come a craftsman (Meister). A high share of highly skilled residents is expected to go along with lower homelessness rates, because education prevents from poverty and, consequently, from homelessness.

Another explanatory variable is the share of inhabitants with a very low income, i.e. below € 700 per month, which is the lowest disclosed income category per district. People with such a low income are more prone to become homeless than those with higher incomes. The related data from the Statistics Agency of North Rhine-Westphalia exhibit some statistically uncertain values, and for a handful of observa-tions values are missing at all. This is why the regression model where this variable is included is estimated with fewer observations. Table 1 provides summary statis-tics on the employed variables.

Table 1 Summary Statistics Variable Obs. Mean Std. Dev. Min Max Share of homeless people in the total district population (in %) 292 0.06 0.07 0 0.57

Net market rents 292 5.50 0.80 3.97 8.34 Vacancy rates among small flats (in %) 292 4.92 2.35 1.00 17.6

Share of long-term unemployed (in %) 259 1.96 1.01 0.04 5.44

Share of residents with monthly income < € 700 (in %) 277 26.51 3.95 16.22 35.27

Share of highly qualified residents (in %) 277 13.46 4.13 6.28 30.62

Source: Authors’ calculations

Using ordinary least-squares estimation, the variables describing the housing mar-ket and the social environment in the districts of North Rhine-Westphalia are re-gressed on the number of homeless people per inhabitant. All variables are meas-ured at the district-level and the independent variables enter in logarithms. The re-gressions are estimated with heteroscedasticity-consistent White standard errors and random effects. This procedure is supported by a panel bootstrap of the Haus-man test which confirms that the model should be estimated with random - rather than with fixed - effects.

4 Results Because of the housing market’s dominant role in driving homelessness in Germany (see, for example, Busch-Geertsema 2005), we first estimate the influence of hous-


ing market variables on homelessness so as to assess the importance of the condi-tions prevailing on the housing market for homelessness, as established in the the-ory section. The number of homeless per thousand inhabitants in each district is regressed on net market rents and vacancy rates among small flats. The results are reported in column 1 of Table 2, with the p-values given in brackets below each co-efficient. As expected, a rise in the level of net market rents in a district leads to an increase of homelessness. More precisely, an increase of the independent variable by 1 % increases the number of homeless per thousand inhabitants by 0.033. Put differently, doubling net market rents leads to 3.3 more persons being homeless among one thousand inhabitants on average, the estimated coefficient being highly significant at the 1 % level. Intuitively, this result is due to particularly poor people facing difficulties to pay their rents from their tight budgets, as the average flat be-comes more expensive. With regard to the model set up in an earlier section, higher costs of housing induce a shift of expenditures from housing towards other goods. In the most extreme case, gross housing consumption falls to zero: As housing be-comes more expensive, the critical income increases (the housing expenditure

line in Figure 2 shifts upwards, so that the lowest bid rent curve which just touches the new housing expenditure line corresponds to a higher income). The less en-dowed households cannot afford to buy housing anymore and become homeless.

Table 2 Regression results (1) (2) (3)

Net market rents 3.251 (0.000)

3.480 (0.000)

3.697 (0.000)

Vacancy rates among small flats -0.127 (0.000)

-0.085 (0.002)

-0.055 (0.049)

Share of residents with monthly income <700 € 0.263

(0.000) 0.217

(0.000)

Share of long-term unemployed 0.062 (0.000)

Share of highly qualified residents -0.428 (0.000)

Constant -4.711 (0.002)

-6.041 (0.000)

-7.163 (0.000)

Random effects Yes Yes Yes Obs. 292 259 277 R² (overall) 0.4012 0.4300 0.4357 p-values are in parentheses Source: Authors’ calculations

The second variable reflecting the conditions on the housing market is the vacancy rate among small flats. Vacancy rates among small flats are particularly relevant in this context, as over 70 % of the homeless would wish to move into a small flat or furnished room (BAGW 2009). Again, the coefficient shows the expected sign and is significant at the 1 % level: A doubling of the vacancy rate results in a decrease of homelessness by 0.13 people per thousand inhabitants on average. The more flats


of a particular category (i.e., small flats in this case) are available on the market, the greater is the chance for a homeless person to find a flat which addresses their par-ticular wants and needs (small and cheap). In tight housing markets, on the other hand, it is more difficult to find a suitable and affordable flat (see above). This can be expected to be particularly difficult for people with low means (which means, among other things, no or only limited access to newspapers and the internet as sources of information on vacancies). The empirical significance of this variable underpins the importance of incorporating vacancy rates into the supply side of any model used to study homelessness, like it is done in the theoretical section of this paper. Neverthe-less, homelessness rates are more sensitive to changes in net market rents than to changes in vacancy rates, as is shown by the estimated coefficients. Altogether, the model explains roughly 40 % of the overall variation in the data.

The first model shows that housing market variables do have a statistically signifi-cant impact on the incidence of homelessness. In order to make out if this relation still holds when controlling for the social environment in a district, two further vari-ables reflecting these social characteristics are included into the model: Like before, the number of homeless per inhabitant is regressed on net market rents and va-cancy rates among small flats. In addition, the share of residents with a monthly income below € 700 and the share of the long-term unemployed among all residents of a given district serve as regressors in this second model. Column 2 of Table 2 shows the results: If the share of residents with a monthly income below € 700 dou-bles, the number of homeless per thousand inhabitants goes up by 0.26, in absolute terms. Naturally, poorer people are more vulnerable to becoming homeless in case they cannot afford to cover their housing expenses anymore (Benjaminsen/Busch-Geertsema 2009: 136). If poverty becomes more prevalent in a district, the share of residents with an income below the critical threshold derived in the model above

rises and more people are at risk of becoming homeless. This shows up as an in-crease in the rate of homelessness.

Empirical evidence shows that almost 70 % of the homeless are long-term unem-ployed (BAGW 2009). Consequently, the share of long-term unemployed in the total district population is also included into the long regression in column 2 in Table 2 to capture the social environment. The estimated coefficient shows that a doubling of the share of long-term unemployed leads to 0.06 more people being homeless per thousand residents. In Germany, the chances of reintegrating long-term unemployed into the labour market are pretty low. Some of the many reasons (see, for instance, Machin/Manning 1999, for an extensive treatment) are that being long-term unem-ployed goes along with stigmatization and signalling effects and, in the course of time, the unemployed get used to their situation. Thus, they face severe difficulties in becoming reemployed. The longer an unemployment spell, the lower state trans-fers become until they eventually fall down to subsistence level, and it becomes more difficult for the long-term unemployed to reach the critical income needed to

avoid being homeless. Moreover, (long-term) unemployed individuals are more likely


to become addicted to alcohol or psychoactive substances (Bönner 1999; Henkel/ Zemlin 2008), which makes it even more difficult to cope with the situation and to remain housed.

Both social variables are statistically significant at the 1 % level. Upon their inclusion into the estimated model, the coefficient of the net market rents is practically un-changed and remains significant at the 1 % level. The coefficient of the vacancy rates among small flats is reduced to almost 2/3 its value compared to the first esti-mated model. Obviously, its influence is reduced when controlling for social factors. Nevertheless, a doubling of the vacancy rates among small flats is associated with a decrease in homelessness by 0.09, and the coefficient is still significant at the 1 % level. This model performs slightly better than the short one and explains 43 % of the overall variation in the data.

Since education is one of the most effective protectors against poverty (Card 1999) and homelessness, we also include into the regression model a variable capturing residents’ educational achievement. Preferably, the model would account for the share of low-skilled people. With the available data, however, this yields misleading results, as drop-outs cannot be distinguished from those still in school or training. As a consequence, the share of highly-skilled residents is included into the model, be-cause this goes along with fewer distortions. Upon the inclusion of the share of a district’s highly qualified residents, the coefficient of the share of long-term unem-ployed becomes insignificant (which is hardly surprising, as each of these variables nearly captures one side of the same coin). Thus, the model is re-estimated with the share of long-term unemployed being omitted, and the results are shown in column 3 of Table 2. As expected, the coefficient of the share of highly skilled residents has a negative sign: Doubling a district’s share of residents who either hold a university or specialist college degree or have completed advanced training to become a craftsman goes along with 0.43 fewer homeless per thousand inhabitants. Besides, the net market rent coefficient remains highly significant and slightly increases, whereas the coefficient of the vacancy rates is further reduced (to about 2/3 its value in the model in column 2), but remains significant at the 5 % level. On the whole, this model performs slightly better than the model in column 2 and explains 43.6 % of the overall variation in the data.

One can be pretty sure that causality indeed runs in the direction postulated by the above models. The homeless represent such a tiny proportion of the population, that it is implausible they exert any influence on the housing market, such as on the net market rents and the vacancy rates among small flats. Rather, the homeless them-selves are being affected by the conditions prevailing on the housing market. Rising rents and low vacancy rates are beyond the scope of their influence and lead to many of them remaining homeless. A similar conclusion holds for the social vari-ables of the model. To the homeless, being homeless is the last resort and means a loss of social status. In countries with a highly developed social security system, it is highly unlikely that somebody will become homeless because of financial or social


troubles and only thereupon lose their job or take on a low-paid job. Instead, in most cases, unemployment and the incapability to pay rent (due to financial dependence on an ex-partner, loss of job or low income) precede becoming homeless. These considerations are supported by the fact that, in Germany, only about 6 % of the homeless are still employed (BAGW 2009).

5 Conclusion Homelessness exerts devastating physical and psychological effects on those af-fected. Thus, there is an obvious need to further investigate the sources of this phe-nomenon so as to provide assistance to the homeless to regain permanent accom-modation and to prevent those who are at risk of being homeless from actually los-ing their dwelling.

In our theoretical model, we build upon the model developed by O’Flaherty (1995) who considers flats of different quality. We extend this model to also include the size of flats. This is especially important when analysing homelessness, since homeless people are mostly looking for flats of a particular - i.e., small - size. Moreover, it is taken into account that there exists a minimum amount of gross housing consump-tion (e.g., a bed and some essentials must fit in). This minimum amount of gross housing consumption may be too high for some people, as the rent for a flat in-creases with size and quality, and they might not be able to afford the minimum supplied. People with an income below the critical threshold for homelessness de-rived in the model seek to consume less than the minimum amount of gross housing consumption. This is not possible, since the demand is not matched by the supply, and they become/are homeless. Another link between the housing market and homelessness is modelled via vacancy rates among small flats. The higher the va-cancy rates among small flats, the more flats which suffice the need of the homeless - and of those at risk of becoming homeless - are available on the market. The im-portance of the theoretically identified factors influencing homelessness is under-pinned by the subsequent empirical analysis.

Using a panel data set, the influence on homelessness exerted by housing market conditions as well as by variables reflecting the social environment is estimated. To the authors’ knowledge, this is the first econometric study of homelessness explicitly focussing on a German region. The results show that there is indeed a significant (causal) correlation between homelessness and the employed housing market and social variables. The present study supports the above-mentioned observation that homelessness increases when the housing market is tight. Rising net market rents and decreasing vacancy rates among small flats both contribute to an increase in homelessness, because flats either become too expensive or too scarce to be af-fordable for those with very low income. Also, a high share of those with a monthly income below € 700 and high long-term unemployment rates go along with a high number of homeless people.


From these results, several specific politically relevant conclusions can be drawn. Although the measures undertaken to reduce homelessness are obviously success-ful (see Introduction), homelessness still exists in North Rhine-Westphalia. It is of paramount importance to study how homelessness can be reduced even further. It becomes obvious that adequate housing market policies may reduce homelessness. For example, council house building should be promoted in order to provide living space for those who cannot afford the minimum amount of gross housing consump-tion supplied on the market and to ease the tightness of housing markets in the lower quality/size segment. Additionally, approximately 70 % of the homeless in North Rhine-Westphalia would like to rent a small flat for a single person. This needs to be considered when promoting council house building. Also, minimizing search frictions on the housing market could be an effective way to reduce home-lessness. Governmental agencies could provide assistance in searching for a suit-able flat, so that the available vacancies can be distributed among accommodation seekers more effectively. Furthermore, earmarked state transfers should be granted, so everybody can reach the critical income needed to rent a flat. These transfers

are already in place in Germany, but it might be helpful to lower the bureaucratic barriers to register for them in order to make them available for a broader group of people. For example, you need a bank account to be able to receive transfers, but without a permanent residence it is very difficult to get a bank account and, conse-quently, state transfers. Furthermore, it may just be too complicated for some people to complete all the paperwork needed to register for transfers. Another point worth mentioning is that homelessness is closely related to being long-term unemployed, which is why an effective way to reduce homelessness is to counteract long-term unemployment more successfully than this is happening at the moment, and to place even more emphasis on education.

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